Building on a Solid Foundation: Adding Relevance and Reproducibility to Neurological Modeling Using Human Pluripotent Stem Cells
Bibliographic record
Abstract
The brain is our most complex and least understood organ. Animal models have long been the most versatile tools available to dissect brain form and function; however, the human brain is highly distinct from that of standard model organisms. In addition to existing models, access to human brain cells and tissues is essential to reach new frontiers in our understanding of the human brain and how to intervene therapeutically in the face of disease or injury. In this review, we discuss current and developing culture models of human neural tissue, outlining advantages over animal models and key challenges that remain to be overcome. Our principal focus is on advances in engineering neural cells and tissue constructs from human pluripotent stem cells (PSCs), though primary human cell and slice culture are also discussed. By highlighting studies that combine animal models and human neural cell culture techniques, we endeavor to demonstrate that clever use of these orthogonal model systems produces more reproducible, physiological, and clinically relevant data than either approach alone. We provide examples across a range of topics in neuroscience research including brain development, injury, and cancer, neurodegenerative diseases, and psychiatric conditions. Finally, as testing of PSC-derived neurons for cell replacement therapy progresses, we touch on the advancements that are needed to make this a clinical mainstay.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".